activity
20182023
most citedDeep learning for synthetic microstructure generation in a materials-by-design framework for heterogeneous energetic materials

120 citations · 222 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2023★ 2 cited

Challenges and opportunities for machine learning in multiscale computational modeling

Phong C. H. Nguyen, Joseph B. Choi, H. S. Udaykumar +1

Many mechanical engineering applications call for multiscale computational modeling and simulation. However, solving for complex multiscale systems remains computationally onerous…

physics.flu-dyn2020

A simulation-derived surrogate model for the vaporization rate of aluminum droplets heated by a passing shock wave

Pratik Das, H. S. Udaykumar

The vaporization rate of aluminum droplets in shocked flows plays a crucial role in determining the energy release rate during the combustion of the aluminized energetic materials.…

cond-mat.mtrl-sci2020★ 120 cited

Deep learning for synthetic microstructure generation in a materials-by-design framework for heterogeneous energetic materials

Sehyun Chun, Sidhartha Roy, Yen Thi Nguyen +3

The sensitivity of heterogeneous energetic (HE) materials (propellants, explosives, and pyrotechnics) is critically dependent on their microstructure. Initiation of chemical reacti…

physics.comp-ph2019★ 17 cited

Structure-Property Linkage in Shocked Multi-Material Flows Using A Level-Set Based Eulerian Image-To-Computation Framework

S Roy, N Rai, O Sen +1

Morphology and dynamics at the meso-scale play crucial roles in the overall macro- or system-scale flow of heterogeneous materials. In a multi-scale framework, closure models upsca…

physics.comp-ph2019★ 37 cited

Modeling meso-scale energy localization in shocked HMX, Part II: training machine-learned surrogate models for void shape and void-void interaction effects

S. Roy, N. K. Rai, O. Sen +3

Surrogate models for hotspot ignition and growth rates were presented in Part I, where the hotspots were formed by the collapse of single cylindrical voids. Such isolated cylindric…

physics.comp-ph2018★ 1 cited

Uncertainty quantification in Eulerian-Lagrangian simulations of (point-)particle-laden flows with data-driven and empirical forcing models

Vasileios Fountoulakis, H. S. Udaykumar, Gustaaf B. Jacobs

An uncertainty quantification framework is developed for Eulerian-Lagrangian models of particle-laden flows, where the fluid is modeled through a system of partial differential equ…